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A Novel Quality Classification Method to Measuring Chemical Contents in Tobacco Leaves

机译:一种测量烟叶化学含量的新质量分类方法

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Tobacco quality classification plays a significant role in its market price determination.Conventional methods including linear discriminant analysis, K-means clustering and BP-neural network flaw in capture the nonlinear structure. The use of support vector machine (SVM) has been shown to be a cost-effective technique. But it is used as a non-preprocessing way for a classification task. This paper extended SVM with kernel principal component analysis (KPCA) for extract valuable discriminatory information. The method is then applied to classify tobacco leaves quality of the Wulong country, one of the most important tobacco planting areas of Chongqing. The classification performance of the proposed method is proven superior compared with other statistical and machine learning methods.
机译:烟草质量分类在其市场价格确定中起着重要作用。常规方法包括线性判别分析,K均值聚类和BP神经网络缺陷来捕获非线性结构。支持向量机(SVM)的使用已被证明是一种经济高效的技术。但是,它用作分类任务的非预处理方式。本文通过内核主成分分析(KPCA)扩展了SVM,以提取有价值的歧视性信息。然后将该方法用于对重庆市最重要的烟草种植区之一的武隆县的烟叶质量进行分类。与其他统计和机器学习方法相比,该方法的分类性能被证明是优越的。

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